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71 - 80 of 145 results for: MS&E

MS&E 299: Voluntary Social Systems

Ethical theory, feasibility, and desirability of a social order in which coercion by individuals and government is minimized and people pursue ends on a voluntary basis. Topics: efficacy and ethics; use rights for property; contracts and torts; spontaneous order and free markets; crime and punishment based on restitution; guardian-ward theory for dealing with incompetents; the effects of state action-hypothesis of reverse results; applications to help the needy, armed intervention, victimless crimes, and environmental protection; transition strategies to a voluntary society.
Terms: Win | Units: 1-3
Instructors: Howard, R. (PI)

MS&E 300: Ph.D. Qualifying Tutorial or Paper

Restricted to Ph.D. students assigned tutorials as part of the MS&E Ph.D. qualifying process. Enrollment optional.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit

MS&E 301: Dissertation Research

Prerequisite: doctoral candidacy.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

MS&E 310: Linear Programming

Terms: Aut | Units: 3
Instructors: Ye, Y. (PI)

MS&E 312: Advanced Methods in Numerical Optimization (CME 334)

Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.
Terms: Aut | Units: 3 | Repeatable for credit
Instructors: Murray, W. (PI)

MS&E 313: Vector Space Optimization

Optimization theory from the unified framework of vector space theory: treating together problems of mathematical programming, calculus of variations, optimal control, estimation, and other optimization problems. Emphasis is on geometric interpretation. Duality theory. Topics: vector spaces including function spaces; Hilbert space and the projection theorem; dual spaces and the separating hyperplane theorem; linear operators and adjoints; optimization of functionals, including theory of necessary conditions in general spaces, and convex optimization theory; constrained optimization including Fenchel duality theory. Prerequisite: MATH 115.
Terms: Aut | Units: 3

MS&E 314: Linear and Conic Optimization with Applications (CME 336)

Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.
Terms: Win | Units: 3
Instructors: Ye, Y. (PI)

MS&E 315: Numerical Optimization (CME 304)

Solution of nonlinear equations; unconstrained optimization; linear programming; quadratic programming; global optimization; general linearly and nonlinearly constrained optimization. Theory and algorithms to solve these problems. Prerequisite: background in analysis and numerical linear algebra.
Terms: Win | Units: 3
Instructors: Murray, W. (PI)

MS&E 316: Discrete Mathematics and Algorithms (CME 305)

Topics: enumeration such as Cayley's theorem and Prufer codes, SDR, flows and cuts (deterministic and randomized algorithms), probabilistic methods and random graphs, asymptotics (NP-hardness and approximation algorithms). Topics illustrated with EE, CS, and bioinformatics applications. Prerequisites: MATH 51 or 103 or equivalents.
Terms: Win | Units: 3
Instructors: Tomlin, J. (PI)

MS&E 318: Large-Scale Numerical Optimization (CME 338)

The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basic factorization and updates. Interior methods. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335.
Terms: Spr | Units: 3
Instructors: Saunders, M. (PI)
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